OPTIMA – Computational Intelligence Methods for Big Mobility Data

OPTIMA – Computational Intelligence Methods for Big Mobility Data


The main objective of this research project is the development of new Computational Intelligence (CI) methodologies, with emphasis on Artificial Neural Networks (ANNs), Evolutionary computation (EC) and Swarm Intelligence (SI), which will be inherently designed tο address the particular characteristics of big mobility data, with the aim of improving public transport services and as a result to enhance people life quality and to maximize environmental benefits. This research project aims to design “attractive” for passengers, and simultaneously “efficient” and “economical” public transports. In order to accomplish this, it is necessary to study tasks such as: bus capacity / passenger load prediction, bus arrival time prediction, bus timetable optimization (scheduling), bus predictive maintenance, bus Eco-driving behavior, safety and collision risk on intelligent transportation systems.


Yannis Theodoridis (Academic Advisor)
Nikos Pelekis (Associate Academic Advisor)
Harris Georgiou (Senior Researcher)
Eva Chondrodima (Senior Researcher)


E. Chondrodima, H. Georgiou, N. Pelekis, Y. Theodoridis, 2021. “Public transport arrival time prediction based on gtfs data”, 7th International Conference on machine Learning, Optimization and Data science (LOD2021), Springer International Publishing, Grasmere, UK. In press


This research is co-financed by Greece and the European Union (European Social Fund – ESF) through the Call entitled “Support for researchers with emphasis on young researchers – cycle B” (Code: EDBM103), which is part of the Operational Program “Human Resources Development, Education and Lifelong Learning”, in the context of the project with MIS 5050503.


Eva Chondrodima, ΜSc, PhD
Senior Researcher,
Data Science Lab, Department of Informatics, University of Piraeus
80 Karaoli & Dimitriou Str., GR-18534 Piraeus, Greece
Phone: (+30)210.4142121, Room n.205 (central building)